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All Journal Techno.Com: Jurnal Teknologi Informasi Telematika Scientific Journal of Informatics Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK POLBENG INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI ComTech: Computer, Mathematics and Engineering Applications JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknologi Dan Sistem Informasi Bisnis JSR : Jaringan Sistem Informasi Robotik Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS Jurnal Dinamika Informatika (JDI) Jurnal Algoritma J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science International Journal of Advances in Artificial Intelligence and Machine Learning
Efrizoni, Lusiana
Program Studi Teknik Informatika, STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Stacked LSTM with Multi Head Attention Based Model for Intrusion Detection Praveen, S Phani; Panguluri, Padmavathi; Sirisha, Uddagiri; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Efrizoni, Lusiana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.764

Abstract

The rapid advancement of digital technologies, including the Internet of Things (IoT), cloud computing, and mobile communications, has intensified reliance on interconnected networks, thereby increasing exposure to diverse cyber threats. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats; however, traditional signature-based and rule-based methods fail to detect unknown or complex attacks and often generate high false positive rates. Recent studies have explored machine learning (ML) and deep learning (DL) approaches for IDS development, yet many suffer from poor generalization, limited scalability, and an inability to capture both spatial and temporal dependencies in network traffic. To overcome these challenges, this study proposes a hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Stacked Long Short-Term Memory (LSTM) networks, and a Multi-Head Self-Attention (MHSA) mechanism. CNN layers extract spatial features, stacked LSTM layers capture long-term temporal dependencies, and MHSA enhances focus on the most relevant time steps, improving accuracy and reducing false alarms. The proposed model was trained and evaluated on the UNSW-NB15 dataset, which represents modern attack vectors and realistic network behavior. Experimental results show that the model achieves state-of-the-art performance, attaining 99.99% accuracy and outperforming existing ML and DL-based intrusion detection systems in both precision and generalization capability.
MODEL PREDIKSI STUNTING PADA BALITA MENGGUNAKAN ALGORITMA NAÏVE BAYES fadillah, m; Gusti Firmansyah, Mulia; Khairuddin, M.; Rahmaddeni; Efrizoni, Lusiana
Jurnal Dinamika Informatika Vol. 13 No. 1 (2024): Jurnal Dinamika Informatika
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v13i1.292

Abstract

This study investigates the use of Naive Bayes algorithm for child stunting classification based on health and nutrition data. This study aims to identify factors that influence the risk of stunting and develop a predictive model that can assist in stunting prevention and intervention. The research methodology includes initial data processing, division of the dataset into training and testing sets, model training using the Naive Bayes algorithm, and evaluation of model performance through metrics such as accuracy, precision, and recall. The results showed that the Naive Bayes model achieved an accuracy of 72.49% for training data and 81.25% for testing data. Confusion matrix analysis shows a precision value of 0.911 and recall of 0.710 for training data; for testing data, the precision value is 0.914 and recall is 0.842. The results show that the Naive Bayes model is able to perform stunting classification quite well, although there are some limitations, such as the conditional independence assumption that may not be met at all times. This research provides insight into how classification models can be used in public health, particularly in efforts to detect and prevent stunting. The results are promising, but further evaluation is needed to optimize the model and ensure that it can be used effectively in the real world.
Perbandingan Algoritma Naive Bayes dan Decission Tree untuk Prediksi Penyakit Kanker Paru-Paru Gusti Firmansyah, Mulia; Khairuddin, M.; fadillah, M; Efrizoni, Lusiana; Rahmaddeni , Rahmaddeni
Jurnal Dinamika Informatika Vol. 13 No. 1 (2024): Jurnal Dinamika Informatika
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v13i1.309

Abstract

In this study, we compared the performance of two machine learning algorithms, Naïve Bayes and Decission Tree, for diagnosing lung diseases using patient health datasets. The main objective of this study is to evaluate the accuracy, precision, recall, and F1 score of the two algorithms to determine which method is more effective in predicting lung diseases. The results showed that the tree classification algorithm outperformed Naïve Bayes in terms of accuracy, reaching 95% in an 80:20 split, compared to the 78% accuracy achieved by Naïve Bayes on the same data. Further analysis showed that most patients in this dataset were high risk with 365 patients, followed by risk with 332 patients, and low risk with 303 patients. The decision tree structure proved to be more effective in handling the complexity of the data and produced more accurate predictions, improving efficiency by creating a new "Risk_Score". These results show that decision trees are a better method than Naïve Bayes for diagnosing lung diseases and can provide a solid foundation for developing accurate machine learning models for future health research.
Penerapan K-Means Clustering untuk Mengelompokkan Risiko Diabetes Berdasarkan Gaya Hidup dan Kesehatan Sigit, Rapel Aprilius; Rio, Unang; Efrizoni, Lusiana; Ali, Edwar
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13292

Abstract

Diabetes mellitus is a chronic disease with a globally increasing prevalence, driven by modern lifestyle changes. Early detection of diabetes risk is crucial in preventing and mitigating long-term complications. This study aims to cluster individuals based on their diabetes risk levels using the K-Means Clustering algorithm by considering lifestyle and health condition attributes. The dataset used was obtained from the Kaggle platform, consisting of 5,452 entries and 22 attributes. The pre-processing stage involved data cleaning, normalization, and manual feature selection. The optimal number of clusters was determined using the Elbow Method, which indicated the best result at k = 3. Cluster quality evaluation was performed using the Davies-Bouldin Index (DBI), which yielded a score of 0.7678, indicating a reasonably good level of cluster compactness and separation. The final output formed three risk clusters: low, medium, and high, with distributions of 424, 819, and 615 records, respectively. This segmentation is expected to serve as a basis for healthcare institutions in designing more targeted and data-driven preventive interventions.
Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning Lusiana Efrizoni; Sarjon Defit; Muhammad Tajuddin; Anthony Anggrawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1851

Abstract

Ektraksi fitur dan algoritma klasifikasi teks merupakan bagian penting dari pekerjaan klasifikasi teks, yang memiliki dampak langsung pada efek klasifikasi teks. Algoritma machine learning tradisional seperti Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression telah berhasil dalam melakukan klasifikasi teks dengan ektraksi fitur i.e. Bag ofWord (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Documents to Vector (Doc2Vec), Word to Vector (word2Vec). Namun, bagaimana menggunakan vektor kata untuk merepresentasikan teks pada klasifikasi teks menggunakan algoritma machine learning dengan lebih baik selalumenjadi poin yang sulit dalam pekerjaan Natural Language Processing saat ini. Makalah ini bertujuan untuk membandingkan kinerja dari ekstraksi fitur seperti BoW, TF-IDF, Doc2Vec dan Word2Vec dalam melakukan klasifikasi teks dengan menggunakan algoritma machine learning. Dataset yang digunakan sebanyak 1000 sample yang berasal dari tribunnews.com dengan split data 50:50, 70:30, 80:20 dan 90:10. Hasil dari percobaan menunjukkan bahwa algoritma Na¨ıve Bayes memiliki akurasi tertinggi dengan menggunakan ekstraksi fitur TF-IDF sebesar 87% dan BoW sebesar 83%. Untuk ekstraksi fitur Doc2Vec, akurasi tertinggi pada algoritma SVM sebesar 81%. Sedangkan ekstraksi fitur Word2Vec dengan algoritma machine learning (i.e. i.e. Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression) memiliki akurasi model dibawah 50%. Hal ini menyatakan, bahwa Word2Vec kurang optimal digunakan bersama algoritma machine learning, khususnya pada dataset tribunnews.com.
Co-Authors -, Dwi Haryono Afrinanda, Rizky Agung Marinda Agus Tri Nurhuda Agustin Agustin Agustin Agustin Agustin Agustin, Endy Wulan Ahmad - Fauzan Ahmad Fauzan Ahmad Rizali Anam, M Khairul Andhika, Imam Anthony Anggrawan Anugraha, Yoga Safitra Aprilia, Fanesa Arifin, Muhammad Amirul Armoogum , Sheeba Aulia, Rahma Azhari, Zahra Cikita, Putri Dadynata, Eric Deni, Rahmad Devi Puspita Sari, Devi Puspita Dewi, Deshinta Arrova Dhini Septhya Djamalilleil, Said Azka Fauzan Edwar Ali Erlinda, Susi Ermy Pily, Annisa Khoirala ester nababan fadillah, m Fadly Fadly Farhan Pratama Fauzan, Aulia Filza Izzati Finanta Okmayura Firdaus, Muhammad Bambang Firman, Muhammad Aditya Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Gusti Firmansyah, Mulia Habibie, Dedi Rahman Hadi Asnal, Hadi Handayani, Nadya Satya Haviluddin Haviluddin Helda Yenni, Helda Hidaya Spitri Hutasoit, Josua Iftar Ramadhan Ihsan, Raja Muhammad Ike Yunia Pasa Irwanda Syahputra Julianti, Nadea Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Karpen Kartina Diah K. W. Khairuddin, M. Kharisma Rahayu Koko Harianto Kurniawan, Tri Basuki Lathifah, Lathifah Lestari, Fika Ayu Lili Marlia M. Azzuhri Dinata M. Irpan Marhadi, Nanda Maulana, Fitra Melva Suryani Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Syaifullah MUHAMMAD TAJUDDIN Munawir Munawir Muslim Muslim Nanda, Annisa Nasution , Zikri Hardyan Novfuja, Elma Nurul fadillah, Nurul Oktavianda Panguluri, Padmavathi Praveen, S Phani Purnama, Muhammad Adji Putantri, Nazlah Sari Putra, Febrianda Putri, Adinda Dwi Putri, Siti Faradila R. Guntur Surya Yuwana - Rabbani, Salsabila Rahmaddeni , Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni, - Rahmiati Rahmiati Rais Amin Ramadhani, Jilang Rati Rahmadani Ratna Andini Husen Revaldo, Bagus Tri Riadhil Jannah Rini Yanti, Rini Risky Harahap Risman Risman Rizki Astuti Rohmatulloh, Vanda Rometdo Muzawi, Rometdo Safitri, Dea Sahelvi, Elza Sapina, Nur Sapitri, Riska Mela Sari, Atalya Kurnia Sarjon Defit Sarjon Defit Setiawan , Andri Shahreen Kasim, Shahreen Sholekhah, Fitriana Sigit, Rapel Aprilius Sirisha, Uddagiri Sularno Supian, Acuan Susandri, Susandri Susanti Susanti Susanti, Susanti Susi Erlinda Syahrul Imardi Syarifuddin Elmi Tahiyat, Hafsah Fulaila Tashid Tawa Bagus, Wahyu Torkis Nasution Tri Putri Lestari, Tri Putri Tri Revaldo, Bagus Triyani Arita Fitri Try Puspa Siregar, Farida Ulfa, Arvan Izzatul Unang Rio Uthami, Kurnia Vindi Fitria Wirta Agustin Wirta Wirta Yanti, Rini Yoyon Efendi Yulli Zulianda Zahra Azhari Zakaria , Mohd Zaki Zakaria, Mohd Zaki Zega, Wilman Zikri Hadryan nst Zulafwan Zuriatul Khairi Zuriatul Khairi